Ultrasound imaging captures a time series of two-dimensional images (sometimes referred to as “frames”) that each correspond to a position- and orientation-specific slice through an anatomical structure. The temporal nature of ultrasound scanning allows the operator to move the imaging device (typically a hand-held probe) to different positions and/or view angles to fully perceive the three-dimensional nature of the anatomical structure under study. The user may also decide to maintain a constant view angle and position in order to observe temporal changes in the anatomy itself due, for example, to breathing or heart beats.
Three-dimensional ultrasound images are useful because they provide a more complete representation of the anatomy for later review by a physician. They may also be used to direct or modify treatments during image-guided surgery or radiotherapy, for automated segmentation of an anatomical structure, and/or in combination with images obtained using other modalities such as computed tomography (CT) or magnetic resonance imaging (MRI). Conventionally, three-dimensional images consist of a series of voxels in space, with each voxel having color and luminance parameter values and a location in a defined coordinate system.
The most common type of three-dimensional ultrasound image acquisition (referred to as “freehand three-dimensional ultrasound”) uses a two-dimensional probe. The operator sweeps the probe about the patient in the vicinity of the anatomical structure of interest, thus acquiring a time series of two-dimensional ultrasound frames. The frames may then be assigned positions and orientations in three-dimensional space by, for example, tracking the probe using an optical camera, a magnetic tracker, an articulated arm or robot, or by using various signal-processing algorithms on the images themselves to calculate the approximate motion of the probe between successive images. Three-dimensional probes are also available, but they tend to be more costly and have a limited field of view, and may be less flexible in imaging certain anatomical structures.
Many algorithms exist for creating three-dimensional voxel images from a series of two-dimensional freehand ultrasound images having known positions and orientations. Such techniques generally fall into one of two categories, namely “reverse” or “forward” techniques. Reverse techniques traverse each voxel and find the best parameter value(s) to assign to that voxel based on the adjacent pixels of the neighboring two-dimensional slices. The operation of determining which neighboring slices are relevant to a given voxel is time-consuming and costly. One example of the reverse approach is the voxel-nearest-neighbor (VNN) algorithm, in which each voxel is assigned the value of its nearest neighboring pixel. This method is relatively straightforward to implement, but the results tend to be visually unappealing and artifacts can appear in the reconstruction. To improve reconstructed images, some reverse algorithms compute an average of a number of pixels from neighboring slices to estimate mean parameter values for the voxel. This additional step, however, increases the computational cost.
One advantage of the reverse techniques is that they easily port to a multi-threaded processing environment. While each thread requires access to the original ultrasound image frames and their three-dimensional positions, the volume can be divided into several sub-volumes with each thread running on one of the sub-volumes. Another important advantage of the reverse techniques is that they can easily exploit optimizations typically found in modern processors. These allow large increases in speed if the jumps in memory access are small. By traversing the reconstruction volume voxel by voxel, the algorithm can utilize these processor optimizations by making the step between voxels consistent with a small jump in the computer memory.
In contrast, “forward” reconstruction techniques start with the first ultrasound frame and cycle through the pixels of that frame, while inserting the pixel values into the appropriate voxels. This process is then repeated for each frame. This approach is more straightforward than the reverse techniques because the algorithm only has to cycle through each pixel, find the correct voxel in three dimensions, and insert the pixel value at that voxel. If multiple pixels intersect a given voxel, their values are typically averaged, which results in the need for a separate volume in memory to keep track of the number of times a pixel “hits” a given voxel, and a larger word-width in the accumulation volume. One drawback of forward techniques is that if the maximum distance between successive frames is larger than the voxel size, voxels between the successive frames are not filled, resulting in “holes” in the reconstructed volume. This is typically addressed by a second step of the algorithm, referred to as “hole filling,” which applies interpolation to fill the holes from neighboring voxel values. This second step slows down the reconstruction, and can lead to unappealing interpolation effects. Typically, a kernel size is defined for interpolation process. If the kernel size is too large, the result is excessive interpolation that results in a blurred image or artifacts; but if the kernel is made too small, some holes may remain unfilled.
Another drawback of forward methods is that they are not easily adapted to multi-threaded processing environments. This is because multiple memory volumes are needed to avoid interference among the multiple threads during the simultaneous writing of the voxels to memory; writing operations to the same fields at the same time by multiple threads is not possible. Furthermore, keeping multiple volumes in the memory at the same time increases memory costs dramatically. Also, pixels are added to the volume in an unorganized fashion, leading to large memory jumps. Thus, the forward algorithms do not take advantage of modern processor optimizations.
As such, existing reconstruction algorithms have disadvantages that lead to relatively slow processing times, unless sacrifices are made that compromise the reconstruction results. Therefore, a new approach to reconstruction is needed.